Search

This study investigates the factors influencing the adoption of fintech AI among 397 government college students from middle-class families in Haryana, India. Structural equation modeling was employed to study the relationships between the adoption of fintech AI and its predictors, namely Perceived Usefulness, Perceived Privacy and Security, Perceived Ease of Use, and Satisfaction of Fintech AI. This study utilized a cross-sectional research design. In this study, a non-probability convenience sampling method was utilized. The researchers opted for a sample of 397 government college students, consisting of 200 males and 197 females, hailing from middle-class families in Haryana, India. This sampling approach was selected because of its simplicity and ease of access, as it enabled the researchers to effortlessly connect with participants without resorting to random selection. The participants were drawn from various subject streams to guarantee a diverse representation of the student body. Data collection occurred between November 2022 and January 2023. The findings suggest that as perceived usefulness, ease of use, and satisfaction with fintech AI increase, the likelihood of adoption also increases. Conversely, as perceived Privacy and Security increase, the likelihood of adoption decreases. It is important to note that the strength of these relationships varies, with the strongest positive relationship observed between Adoption and Satisfaction of Fintech AI, while the relationships with Perceived Usefulness, Perceived Privacy and Security, and Perceived Ease of Use are weaker. These insights provide valuable information for organizations and policymakers seeking to promote the adoption of fintech AI among college students. Strategies aimed at addressing Privacy and Security concerns, enhancing user satisfaction, and improving perceived usefulness and ease of use may prove effective in increasing the adoption of fintech AI within this demographic.

Abstract Background Retrospective and descriptive molecular epidemiology studies have shown that Mycobacterium tuberculosis whole genome sequencing can identify outbreaks and disease transmission events with higher resolution than conventional epidemiological investigations. Those studies have strengthened our understanding of genomic polymorphisms correlating with person-to-person transmission and helped resolve putative transmission clusters. To date, systematic genomic surveillance programs implemented for M. tuberculosis were only implemented in low-incidence settings. The purpose of this study is to determine whether there is an impact of routine M. tuberculosis whole genome sequencing on tuberculosis case detection in a high-incidence setting. Methods A cluster randomized controlled trial will be performed. Forty-eight rural village groups (or Fokontany) in the Vohibato district of Madagascar will be randomized to one of three interventions arms. Arm 1 (standard of care) involves healthcare facility-based passive case detection with smear microscopy testing. Arm 2 (best practice) consists of active case finding and Xpert MTB/RIF Ultra PCR testing followed by household contact investigations. Arm 3 (novel intervention) includes the same interventions as arm 2, with addition of sputum culture and M. tuberculosis whole genome sequencing for all newly diagnosed cases. In arm 3, molecular suggested putative outbreaks are investigated, and additional TB suspects are appropriately tested. The intervention observational period will be 2 years. The primary outcome will be the number of detected cases/100,000/year in each arm after 1 year of intervention. Discussion This study is designed to determine whether there is an impact of prospective whole genome sequencing-based molecular typing on tuberculosis case detection in high-incidence settings. Investigating potential outbreaks and focusing active case finding in spatiotemporal settings where disease transmission is suggested by genomic typing is hypothesized to improve case detection in rural communities. Trial registration ClinicalTrials.gov NCT05406453 . Retrospectively registered on June 6, 2022.

The agriculture industry has an enormous influence on a nation's economy. Loss of yield due to plant diseases remains a reason, reducing crop quantity and quality. Incorrect diagnosis of crop diseases can result in improper application of chemical pesticides, which promotes immune microbial strains, raises expenses, and triggers fresh outbreaks that are harmful to the economy and the ecosystem. Despite the potential of Machine Learning (ML) and Deep Learning (DL) approaches in plant disease detection, their limited effectiveness results in poor or late disease detection. Resolving this issue is critical, requiring the development of more accurate disease detection methods. This research introduces an innovative approach for the detection of apple leaf diseases utilizing the CNN-based Inception-v3 model. The dataset comprises images taken on location without having any control over the image-capturing settings may provide better relevance to real-world scenarios. The proposed method integrates canny edge detection and watershed transformation to achieve accurate image segmentation, thereby enhancing the identification of disease regions. Additionally, exploratory data analysis was performed, and channel distributions were visualized to understand the dataset's characteristics. To ensure robust evaluation, the model's performance underwent stratified 5-fold cross-validation. The model classified plant images with 84.60% precision, 87.40% recall, 85.00% F1-score, and 94.76% accuracy. Experimental results substantiate the efficacy of the proposed approach, surpassing existing methods in disease classification.

There has been a conscious effort in the past decade to produce a more theoretical account of the use of technology for learning. At the same time, advances in artificial intelligence (AI) are being rapidly incorporated into learning technologies, significantly changing their affordances for teaching and learning. In this article I address the question of whether introduction of AI and associated features such as machine learning is a novel development from a theoretical perspective, and if so, how? I draw on the existing perspective of sociomateriality for learning and argue that the use of AI is indeed different because AI transforms sociomateriality by allowing materiality to take on characteristics previously associated primarily with a human agent, thereby shifting the nature of the sociomaterial assemblage. In this "data and algorithm"-driven AI-based sociomateriality, affordances for "representation and agency" change, thereby modifying "representational and relational practices" that are essential for cognition. The dualities of data/algorithm, representational/ agentic augmentation, and relational/participatory practices act in tandem within this new sociomaterial assemblage. If left unchecked, this new assemblage is prone to perpetuate the biases programmed within the technology itself. Therefore, it is important to take ethical and moral implications of using AI-driven learning technologies into account before their use.

BackgroundThe term “nontraditional students” (NTS) is widely used in higher education research, but its definition varies across studies.ObjectivesThis systematic literature review aims to examine how researchers define NTS in U.S.-based studies and identify potential definitional issues.MethodsWe conducted a systematic review following PRISMA guidelines, searching EBSCO databases (Education Research Complete, Education Full Text, and ERIC) for peer-reviewed articles published between 2018 and 2022. We analyzed 65 papers that met our inclusion criteria to assess the definitions used for NTS. In this systematic literature review we focus on the definitional issues related to how researchers use the term nontraditional students in US-based studies. We review 65 papers from search results containing 432 papers to understand how researchers define nontraditional students. Of the 65 papers reviewed fully, 33 papers included a specific definition of nontraditional students, 15 included an unspecified definition of nontraditional students, and 17 papers did not include a clear definition at all. Our work suggests that researchers use a clearer definition, such as from the NCES, to define nontraditional students and focus their attention on the seven categories given by NCES.

This research’s goal is to identify the variables that influence consumers’ decisions when they buy prescription drugs online. This study investigates the factors influencing consumers’ decisions regarding online pharmacies through a comprehensive investigation of the variables influencing purchasing decisions. The Unified Theory of Adoption and utilize of Technology (UTAUT 2) is extended to include already available two variables; Performance Expectancy and Price Value and extension has been established by including Adoption Intention as a mediator among PE, PV and Consumer Buying Behavior (CBB). Partial least squares (PLS) analysis is used in this study to examine a sample size of 384 users. The results of the study showed that performance expectancy has no direct impact on adoption intentions which is quite opposite to the previous studies’ findings. Also, findings revealed that Price value has strong direct effect on adoption intention. The findings also shed light on the mediating effect of adoption intention influencing consumers’ choices when they buy products from internet pharmacies, providing knowledge to enhance the online pharmacy experience and support industry stakeholders in their strategic decision-making. In light of the findings, this work provides organizations with useful guidance on how to establish online pharmacy platforms and implement recommendations for online purchases.

Entrepreneurship helps to mainstream the disadvantaged. In order to design policy for the promotion of entrepreneurship among disadvantaged people a proper understanding of entrepreneurship is imperative and cognition has been considered as the best interpreter of entrepreneurship. This study aimed to explore the driving forces and hindrances that influence disadvantaged individuals in their decision to initiate entrepreneurial endeavors. We are using a sample of 1173 people taken from the low-income population of India. The data is analyzed through the use of the Logistic regression technique. The result reflects that attitude, subjective norms, self-efficacy, perceived opportunities and role models encourage Indian disadvantaged to start business. This research contributes to two existing theories: challenge-based entrepreneurship and the theory of planned behavior. It also sheds light on a previously understudied group - disadvantaged entrepreneurs in developing economies. This knowledge can be used by policymakers to create programs that encourage entrepreneurship at the grassroots level.

This theoretical review explores the intricate relationship between Denison’s organizational culture and worker productivity within the context of municipalities, with a specific focus on workplace incivility as a potential mediator. Denison’s model, encompassing four key cultural traits - involvement, consistency, adaptability, and mission - provides a comprehensive framework for understanding how organizational culture impacts various organizational outcomes. The main goal of this review is to introduce the conceptual model framework that suggests the connections between the dimensions of Denison’s organizational culture and employee productivity as well as workplace incivility. The effect of Denison’s organizational culture on the productivity of municipal workers will be explored. By combining existing theoretical perspectives and empirical evidence, this study aims to offer a comprehensive understanding of how each dimension of Denison’s model interacts with and influences productivity through incivility in the distinct settings of municipal organizations. Furthermore, this review contributes to both theoretical and practical domains by enhancing our understanding of how Denison organizational culture manifests in municipal settings and its subsequent influence on worker productivity. Moreover, it furnishes a comprehensive insight into workplace incivility, presenting theoretical arguments and substantiating evidence. The discourse advocates for the vigilant monitoring, effective management, and elimination of adaptable organizational cultural practices within municipalities. These efforts are proposed to not only mitigate workplace incivility but also foster an environment conducive to heightened employee performance and productivity.

AbstractThis investigation aims to conduct a bibliometric analysis to amalgamate the publication patterns of auditing practices. As a result, this research scrutinizes the historical, contemporary, and forthcoming studies on auditing practices. The content is sourced from the Scopus database, and following the application of specific selection criteria, a total of 516 documents are chosen for bibliometric scrutiny. Citation analysis is employed to identify the most influential publications and contributors. In order to determine the most productive authors and sources, an analysis of productivity is undertaken. The bibliometric analysis used to discover five principal thematic clusters in auditing practice research. The themes that are extensively referenced include the legitimacy of audits, their function, archival auditing, the proliferation of audits, audit technologies, and the analysis of big data. By means of co-citation analysis, the works that are most frequently referenced are identified, while co-occurrence analysis is employed to identify the current themes that are trending in research on auditing practices. The present trends and clusters of themes encompass sustainability assurance, the role of auditors in financial crises, blockchain technology, professional skepticism, and the detection of fraudulent activities. The future directions for research are presented in the academic papers.

The current study, guided by resource-based and signaling theories, investigated the mediator's function of employer branding in the connection between human resources practices and employee engagement. Data analysis for this research employed PLS-SEM, with a sample size comprising 220 individuals employed in various IT companies within the Delhi-NCR region. The restraint to this probe is that it is transverse analysis and employs quantitative research methodologies. The outcomes of the present work unveiled a direct and statistically substantial relationship among human resource practices and employee engagement. Furthermore, employer branding was identified as a mediator in the relationship between human resource practices and engagement among employees. This research provides targeted guidance for HR professionals and leaders in the IT sector, presenting in-depth insights into human resources dynamics, specifically focusing on employee engagement. Emphasizing the intermediary role of employer branding, our study empowers managers with actionable insights to enhance HR strategies, fostering heightened workplace satisfaction and increased employee engagement for a resilient and thriving workforce.

Background White matter hyperintensities (WMH) and their progression are associated with risk of dementia and stroke, so are an important target for clinical trials. The cost of broad magnetic resonance imaging (MRI) screening to identify eligible individuals, however, limits the feasibility of designing clinical trials targeting WMH. A low‐cost retinal or clinical screening measure before MRI could reduce recruitment costs versus an MRI‐only screening design in a hypothetical clinical trial. Methods and Results Data from the Atherosclerosis Risk in Communities study with valid retinal and WMH measurements (N=1311) were used. To identify a population at greater likelihood of significant WMH on MRI and thus reduce the number of screening MRIs required, we evaluated 3 theoretical prescreening measures: (1) retinal, (2) clinical, (3) combined clinical‐retinal. Given a target sample for clinical trials (N=646), we calculated screening sample sizes based on the proportion within the population having an elevated score for each prescreening measure (separately) multiplied by the proportion of significant WMH among those with that prescreening feature. Recruitment costs were calculated using estimated retinal and MRI cost estimates. Compared with the estimated cost of MRI‐only screening (>$4.24 million, requiring MRI on 6526 participants), prescreening for a high clinical score resulted in total cost of $2.47 million, with an initial screening group of 52 778 participants, with MRI in 3801. A high clinical‐retinal score cutoff resulted in costs of $2.9 million while requiring 13 572 participants, with 3801 completing MRI. Conclusions A 2‐stage design with low‐cost prescreening measures is a promising approach, resulting in reduced theoretical recruitment costs compared with an MRI‐only design.

Value Added Tax is widely used and prominent since it generates significant economic revenue and is very simple to administer. Consumers who purchase various goods and services are subject to a standard consumption tax called VAT, which is gradually collected. Economic growth is necessary for a country to be sustainable in the long run. VAT has been recognized as an essential global tax instrument that fosters economic growth. The study examines the customers’ perception of financial, economic, and social perspectives of VAT in sustainable economic development. This study employed a mixed-methods design, incorporating both quantitative and qualitative methodologies. The data was obtained from 315 respondents in Saudi Arabia via a structured, self-administered questionnaire. Descriptive analysis, correlation analysis, reliability analysis, regression analysis, and ANOVA were employed to analyze the data in SPSS. The study results showed that all factors substantially impacted customers’ perception, and customers perceive that the revenue generated from VAT significantly contributes to a country’s financial, social and economic development. The advice presented would be helpful to scholars and policymakers who are examining the implications of VAT and how to improve compliance.

The metaverse has disrupted the traditional marketing practices and it has potential to transform entire world of marketing activities with thrilling immersive experiences. This study provides an analysis of evolving field of metaverse marketing in the context of information systems using state of the art bibliometric and scientometric tools coupled with machine learning algorithms. Utilizing 257 documents from Scopus database that published between 1996 and 2024, this research maps and unveils the development of metaverse marketing from its inception and the role of information systems in its evolution. The analysis of literature resulted in five main emerging themes of the role of information systems in metaverse marketing research as User Experience, Customer engagement, Convergence of metaverse Technology, Design of virtual goods & experience and Global Social Interaction. The major sub-themes of the study are User Behaviors and Preferences, Branding on virtual environment, Virtual reality, Virtual wearables and Virtual Socialization. This study also reveals the emerging trends and gaps in literature that pave the ways for future research expansion in the information systems and metaverse marketing. Few of the important future research areas identified are understanding user experience, design of immersive customer engagement strategies, customer virtual presence and Security & privacy concerns of the users on metaverse platform.

This paper explores the factors affecting the investment intention of individual real estate investors utilizing the extended theory of planned behavior. With the help of self-administered questionnaire, data from 366 individual investors from India was collected. This data was analysed using two-step structural equation modelling. While significant direct effect of attitude, external influence, financial self-efficacy and perceived financial return was found, interpersonal influence, perceived financial risk, facilitating conditions and financial awareness had no significant direct impact on investment intention. Upon checking the mediating effect of attitude on the factors, all factors influenced investment intention through attitude except facilitating condition and financial awareness. It was also observed that attitude stands out as the most important aspect due to strongest influence on intention directly and also providing mediation to all variables except two. The study guides policymakers and investment institutions to develop strategies and utilize resources in a direction that can bring out a positive outcome by strengthening real estate investors’ investment intentions. It brings out the fact that financial confidence should be boosted by enabling investors to handle and manage their finances which can bring in a positive attitude for investing.